UBRISA

View Item 
  •   Ubrisa Home
  • Faculty of Science
  • Geology
  • Research articles (Dept of Geology)
  • View Item
  •   Ubrisa Home
  • Faculty of Science
  • Geology
  • Research articles (Dept of Geology)
  • View Item
    • Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Coupling NCA dimensionality reduction with machine learning in multispectral rock classification problems

    Thumbnail
    View/Open
    Minerals-11-Paper-2021.pdf (8.424Mb)
    Date
    2021-08-05
    Author
    Sinaice, Brian Bino
    Owada, Narihiro
    Saadat, Mahdi
    Toriya, Hisatoshi
    Inagaki, Fumiaki
    Bagai, Zibisani
    Kawamura, Youhei
    Publisher
    MDPI, https://www.mdpi.com/
    Link
    https://www.mdpi.com/2075-163X/11/8/846
    Rights
    Available under Creative Commons License
    Type
    Published Article
    Metadata
    Show full item record
    Abstract
    Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time consuming and computationally costly rock and mineral identification methods. Therefore, this paper proposes integrating Hyperspectral Imaging, Neighbourhood Component Analysis (NCA) and Machine Learning (ML) as a combined system that can identify rocks and minerals. Modestly put, hyperspectral imaging gathers electromagnetic signatures of the rocks in hundreds of spectral bands. However, this data suffers from what is termed the ‘dimensionality curse’, which led to our employment of NCA as a dimensionality reduction technique. NCA, in turn, highlights the most discriminant feature bands, number of which being dependent on the intended application(s) of this system. Our envisioned application is rock and mineral classification via unmanned aerial vehicle (UAV) drone technology. In this study, we performed a 204-hyperspectral to 5-band multispectral reduction, because current production drones are limited to five multispectral bands sensors. Based on these bands, we applied ML to identify and classify rocks, thereby proving our hypothesis, reducing computational costs, attaining an ML classification accuracy of 71%, and demonstrating the potential mining industry optimisations attainable through this integrated system.
    URI
    http://hdl.handle.net/10311/2485
    Collections
    • Research articles (Dept of Geology) [33]

    DSpace software copyright © 2002-2015  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    @mire NV
     

     

    Browse

    All of UBRISA > Communities & Collections > By Issue Date > Authors > Titles > SubjectsThis Collection > By Issue Date > Authors > Titles > Subjects

    My Account

    > Login > Register

    Statistics

    > Most Popular Items > Statistics by Country > Most Popular Authors